Top Tech Jobs & Startup Jobs in Austin, TX

Serve as NVIDIA's CSP-facing principal for end-to-end performance: drive characterization with hyperscaler engineering teams, gather and synthesize workload feedback, prioritize optimizations across CUDA/NCCL/driver/firmware, validate benchmarks and tools, update stress/profiling tooling, define test strategies, and ensure performance integration and acceptance for customer-representative configurations.

Design and deliver high-performance, area- and power-efficient ASIC/GPU micro-architecture and RTL. Develop agentic AI solutions to craft micro-architecture, implement RTL, and produce fully verified, synthesis/timing-clean designs. Collaborate with designers and tool owners, and build LLM-powered engineering assistants, multi-agent systems, and multi-modal dialogue tools to accelerate chip design workflows.

The role involves benchmarking deep learning models on NVIDIA GPUs, improving software performance, and developing automation tools for performance optimization.

Design and implement Rust-to-GPU compiler backends, IR frameworks, and JIT/AOT toolchains. Lower Rust AST/MIR to MLIR/LLVM/PTX, build GPU-specific optimizers, enforce safe parallel abstractions across host/device boundaries, and expose ergonomic device-side primitives. Drive projects from research to production and support profiling, debugging, and benchmarking workflows.

Design and optimize low-latency C/C++ systems for real-time streaming over 5G, Wi‑Fi, and other wireless networks. Integrate with cellular and platform APIs, analyze packet captures and telemetry to fix latency and packet loss, develop KPIs and test frameworks, build realistic wireless simulations, and partner with cloud, automotive, carrier, and platform teams to deliver scalable connectivity solutions.

Lead design and implementation of scalable communication technologies for AI and HPC. Identify performance bottlenecks, co-design HW/SW, build proofs-of-concept, run experiments and simulations at large GPU-cluster scale to accelerate deep learning and HPC workloads.

The role involves researching and developing GPU-accelerated AI workloads, optimizing algorithms, and collaborating with technical experts to improve hardware performance.

Join the Compute Compiler Team to align NVIDIA's compiler codebases with open-source projects (LLVM/Clang/MLIR), build tooling and workflows for large-scale codebase reconciliation, and develop automation and AI-assisted developer tools to improve productivity, validation, and refactoring across distributed teams.

Lead a team to optimize algorithms for Deep Learning and Machine Learning, define technical initiatives, and collaborate with various teams for hardware and software innovation.

Develop and deploy tools and workflows for engineering. Verify design blocks, automate RTL generation, and improve IP development integration.